CTO, Co-Founder, Predictive Brand Intelligence Venture | Pre-Seed | Equity-Only

Chief Technology Officer \& Co-Founder

Stealth-Mode Predictive Brand Intelligence Venture \| Pre-Seed \| Equity-Only

The Venture

We are building a patent-pending AI platform that resolves the structural failure of enterprise brand analytics: static, reactive sentiment tracking that reports what already happened instead of anticipating what's coming next.

The technology combines

multimodal data stream integration

,

temporal deep learning

, and

real-time scenario analysis

to deliver granular, anticipatory brand sentiment forecasting — modeling longitudinal sentiment dynamics across heterogeneous digital channels, detecting crisis events and anomalies in context, and modulating for cyclical market phenomena and exogenous shocks. Continuous learning and plug-and-play modality expansion let the platform adapt to volatile market conditions without a full retraining cycle.

The addressable market exceeds $24 billion across enterprise brand management, financial sentiment monitoring, and crisis management. The underlying IP, technical thesis, and commercial framework are protected and prepared. We are now assembling the founding C-suite.

The Role

The CTO \& Co-Founder will own the full technical roadmap — from

multimodal data ingestion architecture

through

temporal deep learning models

,

real-time scenario simulation engine

, and production-grade platform infrastructure. True zero-to-one founder seat with end-to-end authority over R\&D, ML architecture, data engineering, and IP execution. The CTO will partner closely with the CEO to translate a defensible predictive sentiment platform into a production-grade, enterprise-deployable asset.

This role is for an operator-scientist with genuine fluency across

multimodal machine learning, temporal/sequential deep learning architectures, and large-scale data pipeline engineering

— not a generalist backend engineer, not a pure NLP researcher, and not a data engineer without modeling depth. The expectation is a credible technical founder who has shipped production systems that fuse heterogeneous, high-velocity data streams into forecasting output at enterprise scale.

A direct word on compensation

This is a founding equity seat. No salary at this stage. We are not looking for a hired CTO — we are looking for a co-owner who will architect the platform from zero and carry it through design-partner deployment, Series A, and category leadership. If that is the seat you have been waiting for, the rest of this posting is written for you.

Core Responsibilities

• Own the end-to-end technical roadmap from MVP through production scale, spanning

multimodal ingestion, temporal deep learning models, scenario simulation engine, and cloud-native platform architecture

.

• Architect the

multimodal data ingestion layer

: social platform APIs, news and media feeds, financial data feeds, review and forum sources, and structured enterprise data — with normalization, deduplication, and real-time streaming discipline across heterogeneous, high-velocity sources.

• Architect the

temporal deep learning core

: sequence models (transformers, temporal convolutional networks, state-space models) for longitudinal sentiment dynamics, crisis event detection and classification in context, anomaly detection tuned to cyclical market phenomena, and exogenous shock modeling.

• Architect the

real-time scenario analysis engine

: forward-looking simulation of sentiment trajectories under alternative conditions, uncertainty quantification, and the modeling discipline that separates genuine forecasting from retrospective trend-fitting.

• Design the

continuous learning and modality-expansion architecture

: online/incremental learning pipelines that adapt to new data and volatile market conditions without full retraining, and a plug-and-play framework for adding new data modalities without re-architecting the core model.

• Lead the cloud-native platform architecture: Kubernetes, Docker, Terraform IaC, multi-region auto-scaling, streaming data infrastructure (Kafka or equivalent), and observability tooling appropriate to real-time enterprise dashboards.
• Lead data compliance and licensing architecture: GDPR-compliant data sourcing, platform API terms-of-service adherence across social and news data sources, and SOC 2 Type II readiness for enterprise and financial services buyers.
• Drive IP execution: continuation filings, claim-chart development around the

multimodal temporal sentiment forecasting

claim space, prior-art analysis against Brandwatch, Talkwalker, and Zignal Labs, and trade-secret architecture around model training and modality-expansion design.

• Lead technical engagement with design-partner enterprises, validating forecast accuracy and early-warning lead-time against real-world crisis and market events.
• Build and lead the founding technical team across ML engineering, data engineering, NLP, platform engineering, and applications engineering.
• Serve as the principal technical voice in investor diligence, enterprise customer technical due diligence, and strategic partnership conversations.

Required Qualifications

• 8\+ years shipping production ML/AI systems at scale, with substantive experience in NLP, time-series forecasting, or multimodal machine learning categories.
• Direct production experience with temporal/sequential deep learning architectures

— transformers, RNN/LSTM variants, temporal convolutional networks, or state-space models — applied to forecasting or anomaly detection at scale. Research-only exposure without production shipping is not qualified.

• Direct production experience with multimodal data fusion

— combining text, structured, and time-series signals into a unified modeling substrate. Single-modality NLP experience alone is not qualified.

• Demonstrable architectural fluency across cloud-native infrastructure: Kubernetes, Docker, Terraform, streaming data pipelines (Kafka or equivalent), and multi-region auto-scaling.
• Direct experience with high-volume, high-velocity data ingestion from heterogeneous external sources, including social and news platform APIs.
• Working experience with online/incremental learning and model retraining discipline appropriate to volatile, non-stationary data environments.
• Zero-to-one leadership track record: a prior CTO, founding engineer, or technical lead role at an AI/ML SaaS venture, taking technology from architectural design through production deployment.
• Working familiarity with data licensing constraints and platform API terms-of-service considerations relevant to social and news data aggregation at scale.
• Graduate degree (Ph.D. preferred) in computer science, machine learning, computational linguistics, statistics, or adjacent field.

Preferred Qualifications

• Prior exit (acquisition or IPO) as a technical founder or early technical leader in social listening, brand analytics, or NLP/forecasting SaaS — Brandwatch (Cision), Talkwalker, Meltwater, Zignal Labs, NetBase Quid, or comparable lineage.
• Direct operating experience inside a tier-one communications, martech, or financial data corporate (Cision, Meltwater, Bloomberg, FactSet).
• Named inventor on granted patents in multimodal machine learning, temporal forecasting, or sentiment analysis methodologies.
• Published or patent-cited record in

NeurIPS, ICML, ACL, EMNLP, KDD

, or equivalent venues, particularly in time-series forecasting, multimodal fusion, or applied NLP.

• Hands-on experience with financial time-series modeling or market-signal forecasting, given the investor relations and financial sentiment monitoring use case.
• Operating familiarity with crisis event detection systems and the practical engineering of low-false-positive anomaly detection in noisy, high-volume social data.
• Experience architecting systems for enterprise dashboard delivery with strict latency and reliability requirements.

Compensation Structure

• Co-Founder equity. Material, vesting on standard terms with appropriate cliff and acceleration.
• No salary at pre-seed stage. Cash compensation reviewed and instated at institutional close.
• Founder-level participation in subsequent funding rounds.